Crowd-enabled Processing of Trustworthy, Privacy-Enhanced and PersonalisedLocation Based Services with Quality Guarantee

Publication Year: 2018 Publication Type : JournalArticle


We propose a novel approach for enabling trustworthy, privacy-enhanced and personalized location based services (LBSs) that find nearby points of interests (POIs) such as restaurants, ATM booths, and hospitals in a crowdsourced manner. In our crowdsourced approach, a user forms a group from the crowd and processes the LBS using the POI knowledge of the group members without involving an external service provider. We use personalized rating in addition to the distance of a POI for finding the answers of the location based queries. The personalized rating of a POI is computed using individual POI ratings given by the group members and the query requestor’s trust and similarity scores for the group members. The major challenges for the crowdsourced data are incompleteness and inaccuracy, which may result in lower quality answer for the LBS. In this paper, we first present techniques to select knowledgeable group members for processing LBSs and thereby increase the accuracy and the confidence level of the query answers. We then develop efficient algorithms to process LBSs in real time and enhance privacy by reducing the number of the group members’ POIs shared with the query requestor. Finally, we run extensive experiments using real datasets to show the efficiency and effectiveness of our approach.


    author = {Tanzima Hashem and Rubaba Hasan and Flora D. Salim and Mehnaz Tabassum Mahin},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    doi = {10.1145/3287045},
    journal = {IMWUT},
    number = {4},
    pages = {167:1--167:25},
    timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
    title = {Crowd-enabled Processing of Trustworthy, Privacy-Enhanced and Personalised Location Based Services with Quality Guarantee},
    url = {},
    volume = {2},
    year = {2018}


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